Eager to avoid the kind of turmoil that roiled the financial industry in 2008, European Union officials introduced a series of regulatory requirements aimed at protecting the clients of financial institutions. These regulations have tight deadlines that often change frequently – creating a rolling set of hard-to-meet deadlines.
Currently, many of financial services firms are focused on delivering against the MIFID II regulation that comes into force in early 2018. These global organizations aren’t just having to deal with EU regulation. Others, such as the U.S. SEF regulation, need to be completed, and soon focus will switch to CCAR and EU GDPR. There is no doubt that there will be many regulations – either new or evolving – that financial organizations will need to comply with in the next couple years.
This ever-increasing regulatory burden has put a growing strain on technology and the business within these organizations. How can businesses meet these needs in a timely and cost effective manner, while remaining competitive? The key is agility. Specifically, in the case of regulatory reporting, it is being able to manage your data in as quick and efficient a manner as possible.
Many regulations require a single view of the “health” of the organization, which directly means consolidating terribly fragmented data from across vast silos.
Projects to rectify this scenario and create a single view of the data have often failed when taking the “usual” approach of data integration, namely using ETL and relational database infrastructure to create a new version of data that is fit for purpose.
The nature of relational databases and their requirement to predefine the structure of the data – or schema — adds a lot of complexity when working on data integration projects. Each of the upstream systems has a life cycle of its own independent of the target data integration schema. Changes to the upstream system mean changes to the target schema and related ETL infrastructure; the more systems to integrate, the greater the challenge to define the one schema that all parties with the organization agree on.
This is further exacerbated by having many of these systems, as in the case of most large organizations and especially true of complex global financial enterprises.
I’ve seen projects literally take years to design an all-encompassing relational database schema that is meant to meet all organizational needs, only to see the project ultimately fail. Of course it would, anyone can see that, because when we are outside looking in, its immediately obvious that the only certainty we have is that things – the landscape, the political environment, the business drivers – will change.
Enterprises need to be able to bend and flex with structures and processes, which is difficult to do with relational and ETL technologies.
Many businesses have grown organically over time and through acquisition and are consequently very siloed. The idea of integrated data has not been at the forefront organisational change despite knowing that it will create value for the business.
Planning for change makes sense, especially since businesses don’t know how regulations will change, when new opportunities will appear, or what risks they’ll need to mitigate against.
Many businesses accommodate those opportunities or risks by effectively creating a new piece of systems infrastructure to deal with the challenge. These new data silos continue to fragmented data even further across the organization, making the problem worse.
Data silos are effectively the antithesis of organisational agility. What you need is a viable alternative: Integrate your data today to answer the questions of tomorrow. It is therefore no surprise that organizations are seeking alternatives to the traditional approach of data integration. IT leaders have been repeatedly stung by failed projects when it comes to integrating data using the traditional approach.
The panacea for any organization surely has to be a consolidated view of all of its data in a repository that they can query, search, discover and analyze.
So how do we achieve the panacea? If we had a blank canvas, which I know doesn’t exist, how would we build our businesses information architecture? This architecture is at the core of everything we do as a business, while technology is the implementation of the business.
Here at MarkLogic, we’ve been working hard to rectify the problem of siloed data by building a single platform that will allow businesses to consolidate search, and discover, report, and analyze their data. We also have tools to help customers harmonize data, extract meaning, and provide a simplified view of data from across their organizations.
This approach is extremely effective within the financial services environment, where regulatory requirements are putting incredible strain on technology professionals. Previously, we have seen teams attempt to define cross-organizational schemas for regulatory compliance that fail when they could not agree on the canonical model ahead of time.
With the MarkLogic approach, they brought data together and started processing and harmonising it for downstream systems and transforming it into regulatory reports. This increased organizational agility and improved data governance as well as put an end to new data silos for every project. These teams are building capabilities around a flexible platform that, in the long run, will pay them dividends in simplified infrastructure and lower costs.
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